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1.  Prediction error and accuracy of intraocular lens power calculation in pediatric patient comparing SRK II and Pediatric IOL Calculator 
BMC Ophthalmology  2010;10:20.
Background
Despite growing number of intraocular lens power calculation formulas, there is no evidence that these formulas have good predictive accuracy in pediatric, whose eyes are still undergoing rapid growth and refractive changes. This study is intended to compare the prediction error and the accuracy of predictability of intraocular lens power calculation in pediatric patients at 3 month post cataract surgery with primary implantation of an intraocular lens using SRK II versus Pediatric IOL Calculator for pediatric intraocular lens calculation. Pediatric IOL Calculator is a modification of SRK II using Holladay algorithm. This program attempts to predict the refraction of a pseudophakic child as he grows, using a Holladay algorithm model. This model is based on refraction measurements of pediatric aphakic eyes. Pediatric IOL Calculator uses computer software for intraocular lens calculation.
Methods
This comparative study consists of 31 eyes (24 patients) that successfully underwent cataract surgery and intraocular lens implantations. All patients were 12 years old and below (range: 4 months to 12 years old). Patients were randomized into 2 groups; SRK II group and Pediatric IOL Calculator group using envelope technique sampling procedure. Intraocular lens power calculations were made using either SRK II or Pediatric IOL Calculator for pediatric intraocular lens calculation based on the printed technique selected for every patient. Thirteen patients were assigned for SRK II group and another 11 patients for Pediatric IOL Calculator group. For SRK II group, the predicted postoperative refraction is based on the patient's axial length and is aimed for emmetropic at the time of surgery. However for Pediatric IOL Calculator group, the predicted postoperative refraction is aimed for emmetropic spherical equivalent at age 2 years old. The postoperative refractive outcome was taken as the spherical equivalent of the refraction at 3 month postoperative follow-up. The data were analysed to compare the mean prediction error and the accuracy of predictability of intraocular lens power calculation between SRK II and Pediatric IOL Calculator.
Results
There were 16 eyes in SRK II group and 15 eyes in Pediatric IOL Calculator group. The mean prediction error in the SRK II group was 1.03 D (SD, 0.69 D) while in Pediatric IOL Calculator group was 1.14 D (SD, 1.19 D). The SRK II group showed lower prediction error of 0.11 D compared to Pediatric IOL Calculator group, but this was not statistically significant (p = 0.74). There were 3 eyes (18.75%) in SRK II group achieved acccurate predictability where the refraction postoperatively was within ┬▒ 0.5 D from predicted refraction compared to 7 eyes (46.67%) in the Pediatric IOL Calculator group. However the difference of the accuracy of predictability of postoperative refraction between the two formulas was also not statistically significant (p = 0.097).
Conclusions
The prediction error and the accuracy of predictability of postoperative refraction in pediatric cataract surgery are comparable between SRK II and Pediatric IOL Calculator. The existence of the Pediatric IOL Calculator provides an alternative to the ophthalmologist for intraocular lens calculation in pediatric patients. Relatively small sample size and unequal distribution of patients especially the younger children (less than 3 years) with a short time follow-up (3 months), considering spherical equivalent only.
doi:10.1186/1471-2415-10-20
PMCID: PMC2936388  PMID: 20738840
2.  Specific detection of fungal pathogens by 18S rRNA gene PCR in microbial keratitis 
BMC Ophthalmology  2008;8:7.
Background
The sensitivity and specificity of 18S rRNA polymerase chain reaction (PCR) in the detection of fungal aetiology of microbial keratitis was determined in thirty patients with clinical diagnosis of microbial keratitis.
Methods
Corneal scrapings from patients were used for Gram stain, culture and PCR analysis. PCR was performed with primer pairs targeted to the 18S rRNA gene. The result of the PCR was compared with conventional culture and Gram staining method. The PCR positive samples were identified by DNA sequencing of the internal transcribed spacer (ITS) region of the rRNA gene. Main outcome measures were sensitivity and specificity of PCR in the detection of fungus in corneal keratitis.
Results
Combination of microscopy and culture gave a positive result in 11 of 30 samples of microbial keratitis. PCR detected 10 of 11 samples that were positive by conventional method. One of the 19 samples that was negative by conventional method was positive by PCR. Statistical analysis revealed that the PCR to have a sensitivity of 90.9% and specificity of 94.7% in the detection of a fungal aetiology in microbial keratitis.
Conclusion
PCR is a rapid, sensitive and useful method to detect fungal aetiology in microbial keratitis.
doi:10.1186/1471-2415-8-7
PMCID: PMC2413208  PMID: 18445283

Results 1-2 (2)